The sentiment of a digital image is important piece of information that one should understand. This is particularly true for artists, authors, and multimedia content publishers. For example, consider the example scenario where an article to be published is intended to convey a certain sentiment, such as joy or sadness, and the article is to be accompanied by a digital image showing the faces of a number a people. The article could be, for instance, a fictional story, and the photo could be one or more persons representing characters in the story. Of course numerous other multimedia digital content scenarios are possible, including non-fictional articles, reports, marketing and promotional literature, and presentations, to name a few examples, any of which can be combined with digital imagery including one or more faces. In any such cases, if the facial expressions of the people in the image don't quite match the sentiment of the content, then the author may wish to make a small but relevant correction to the image, so that the sentiment reflected by the expressions of the people in the image is more consistent with the content of the article. To address this issue, the author (or publisher, as the case may be) will have to access the image in question with an image editing application and proceed to manually correct each and every facial expression that doesn't match with the context or the article. This can be a difficult and time consuming process.